• Title: Introduction to Privacy
• Provided by: Dept. of Computer Engineering, Myongji University
• Lead by: Minho Shin (mhshin@mju.ac.kr, Rm5742)
• Period: Fall semester, 2017
• Location: 5701 at 5th Engineering Building
• Time: Tuesdays, 10am to 1pm
• Type: Graduate Seminar
• Goal of the class
  • This class aims to familiarize students with current research topics in Privacy area

• Resources

Participants

• Goal: Analyze the privacy levels of data using ARX + alpha
• Steps
  1. Choose your data (already done)
     1. 권유진: adult
     2. Zhong: atus
     3. 장민해: cup
     4. 이수정: fars
     5. 노건: ihis
  2. Download your data and VHG
  3. Open the data in ARX
  4. Choose quasi-identifiers
  5. Choose one sensitive attribute
  6. Import VHGs
  7. Check if VHG is valid. If not, fix it.
     1. Generalization steps should be in detail
  8. Run de-identification algorithms
     1. run k-anonymity with k=3, 5, 10
        1. choose suppression limit as you wish
     2. run distinctive l-diversity with l=3, 5, 10
     3. run entropy l-diversity with l=3, 5, 10
     4. run t-closeness with your choice of t
  9. For each de-identified data do
     1. Compute Min/Max/Avg EQ group sizes
     2. Compute Min/Max/Avg # of different sensitive values
     3. Compute Min/Max/Avg entropy
     4. Compute GenInfoLoss
     5. Compute the privacy levels
        1. Compute identity-disclosure level
           • 
        2. Compute attribute-disclosure level
           • 
        3. Compute inference-disclosure level
           • 
        4. Analyze the results of privacy levels
  10. Discuss the results

• 개인정보_비식별_제도_해외_동향_및_사례
• 데이터_비식별화를_이용한_빅데이터_통합
• a_globally_optimal_k-anonymity_method_for_the_de-identification_of_health_data
• data_anonymization_using_pseudonym_system_to_preserve_data_privacy
• efficient_systematic_clustering_method_for_k-anonymization
• implementation_and_evaluation_of_an_efficient_secure_computation_system_using_r_for_healthcare_statistics
• on_distributed_k-anonymization
• privacy_preserving_e-health_data_sharing_in_cloud
• secure_k-anonymization_over_encrypted

Through Augmented Reality}}

• (Taken by Abbas)Color Scheme Adaptation to Enhance User Experience on Smartphone Displays Leveraging Ambient Light
• (Taken by Hussein)ShakeUnlock: Securely Transfer Authentication States Between Mobile Devices
• Utility Maximization for Multimedia Data Dissemination in Large-Scale VANETs
• Analysis of Multi-Hop Probabilistic Forwarding for Vehicular Safety Applications on Highways
• A Model-Driven Approach to Enable Adaptive QoS in DDS-Based Middleware
• A Secure Communication Protocol with Privacy-Preserving Monitoring and Controllable Linkability for V2G
• A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT
• Efficient Privacy-Preserving Dual Authentication and Key Agreement Scheme for Secure V2V Communications in an IoV Paradigm
• (Taken by Zhong)Certificate sharing system for secure certificate distribution in mobile environment
• Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing
• (Taken by Henock)The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices
• (Taken by Hussein)Tourists responses to mobile augmented reality travel guides: The role of emotions on adoption behavior
• Secure authentication scheme for IoT and cloud servers
• (Taken by Zhong)Appstract: On-The-Fly App Content Semantics With Better Privacy
• (Taken by Gun)SALVE: Server Authentication with Location VErification
• Unsupervised Recognition of Interleaved Activities of Daily Living through Ontological and Probabilistic Reasoning
• (Taken by Abbas)Quantum computing: An IBM perspective